Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions, and experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis. In this work, we present Quokka, a novel bioinformatics tool that allows users to rapidly and accurately identify human kinase-regulated phosphorylation sites. Quokka provides users with multiple prediction models, including a variety of sequence scoring functions and a logistic regression algorithm. A variety of experimental studies based on both benchmark and independent test datasets demonstrate that Quokka improves the prediction performance compared with state-of-the-art computational tools for phosphorylation prediction. We anticipate that Quokka will provide users with high-quality predicted human phosphorylation sites for hypothesis generation and further biological validation.
The benchmark and independent test datasets for phosphorylation sites are available for download here
We have investigated the phosphorylation sites for human proteome with Quokka.
The proteome-wide prediction results for 11 kinase families can be downloaded here
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